Paper Presentation & Seminar Topics: Abstract: The stream cipher Rabbit was _rst presented at FSE 2003 [6]. In the paper at hand, a full security analysis of Rabbit is given, focusing o

Abstract: The stream cipher Rabbit was _rst presented at FSE 2003 [6]. In the paper at hand, a full security analysis of Rabbit is given, focusing o

Abstract:
Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. In this paper, we introduce a new measure w-support, which does not require preassigned weights. It takes the quality of transactions into consideration using link-based models. A fast miming algorithm is given, and a large amount of experimental results are presented.
Generally, Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Association rule mining aims to explore large transaction databases for association rules, which may reveal the implicit relationships among the data attributes. The classical model of association rule mining employs the support measure, which treats every transaction equally. Much effort has been dedicated to association rule mining with pre-assigned weights.
However, most data types do not come with such pre assigned weights, such as Web site click-stream data. There should be some notion of importance in those data. For instance, transactions with a large amount of items should be considered more important than transactions with only one item. Current methods, though, are not able to estimate this type of importance and adjust the mining results by emphasizing the important transactions.
So w-support, a new measure of item sets in databases with only binary attributes. The basic idea behind w-support is that a frequent item set may not be as important as it appears, because the weights of transactions are different. These weights are completely derived from the internal structure of the database based on the assumption that good transactions consist of good items
A new measurement framework of association rules based on w-support is proposed. Experimental results show that w-support can be worked out without much overhead, and interesting patterns may be discovered through this new measurement.